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The Rough Granular Approach to Classifier Synthesis by Means of SVM

  • Jacek Szypulski
  • Piotr ArtiemjewEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9437)

Abstract

In this work we exploit the effects of applying methods for constructions of granular reflections of decision systems developed up to now in the framework of rough mereology, along with kernel methods for the building of classifiers. In this preliminary report we present results obtained with the SVM classification with use of the RBF kernel. The approximation metod we use is the optimized \(\varepsilon \) concept dependent granulation. We experimentally verify the validity of this new approach with test data: Wisconsin Diagnostic Breast Cancer, Fertility Diagnosis, Parkinson Disease and the Prognostic Wisconsin Breast Cancer Database. The results are very promising as the obtained accuracy is not diminished but the size of the granular decision system is radically diminished.

Keywords

Rough sets Decision systems SVM Granular rough computing 

Notes

Acknowledgement

The author wishes to thank Professor Lech Polkowski for kind help and advice. The research has been supported by grant 1309-802 from Ministry of Science and Higher Education of the Republic of Poland.

References

  1. 1.
    Artiemjew, P.: On strategies of knowledge granulation and applications to decision systems, Ph.D. Dissertation, Polish Japanese Institute of Information Technology. L. Polkowski, Supervisor, Warsaw (2009)Google Scholar
  2. 2.
    Artiemjew, P.: A review of the knowledge granulation methods: discrete vs continuous algorithms. In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems. ISRL, vol. 43, pp. 41–59. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  3. 3.
    Boser, B.E., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of Vth International Workshop on Computational Learning Theory, pp. 144–152. ACM Press (1992)Google Scholar
  4. 4.
    Chang, C.-C., Lin, C.-J.: LIBSVM, a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvmCrossRefGoogle Scholar
  5. 5.
    Polkowski, L.: Formal granular calculi based on rough inclusions (a feature talk). In: Proceedings 2005 IEEE International Conference on Granular Computing GrC 2005, pp. 57–62. IEEE Press (2005)Google Scholar
  6. 6.
    Polkowski, L.: Granulation of knowledge in decision systems: the approach based on rough inclusions. The method and its applications. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 69–79. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  7. 7.
    Polkowski, L.: Formal granular calculi based on rough inclusions (a feature talk). In: Proceedings 2006 IEEE International Conference on Granular Computing GrC 2006, pp. 57–62. IEEE Press (2006)Google Scholar
  8. 8.
    Polkowski, L.: Approximate Reasoning by Parts. An Introduction to Rough Mereology. ISRL, vol. 20. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  9. 9.
    Polkowski, L., Artiemjew, P.: Granular Computing in Decision Approximation - An Application of Rough Mereology. ISRL, vol. 77. Springer, Switzerland (2015). ISBN 978-3-319-12879-5, pp. 1–422 zbMATHGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of Warmia and MazuryOlsztynPoland

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